Detecting Internet of Things Attacks by Using Hybrid Learning and Feature Selection Method

Abstract

Internet of Things (IoT) produces an enormous amount of data, which is used in all areas of our lives and increases the number of data on the Internet with each passing day. Smart watches, robot vacuum cleaners, refrigerators with cameras, and more can all be considered IoT devices. Ease of access to the Internet provides people with advantages as well as disadvantages. Malware and intruders have easier access to the devices we use and our information via the internet. At this point, data security gains great importance especially in IoT devices because accessing our personal data via smart watches or refrigerators we use can pose a great threat to individuals and their families. This study focus the importance of data preprocessing and developing a hybrid machine learning-based intrusion detection system (IDS) for IoT. Decision Tree, which is a popular machine learning algorithm, and n_Balot dataset were preferred for investigations. Accordingly, it is aimed to create a hybrid model by applying K-means and Decision Tree algorithms to the n_Balot dataset with under sampling and feature selection. In the data preprocessing, feature selection was performed with Chi-Square method and under sampling performed with RandomOverSampling method. Then, clustering was done by applying K-means to the processed dataset, and the results obtained with the clustering were classified with the Decision tree algorithm. As a result of the study, while the error rate was 0.39% in the predictions made only with the decision tree, the error rate was reduced to 0.01% with the developed hybrid model.